a glimpse at your food future via Nestle…

A kit for Nestle Japan’s nutritional drink. Photographer: Kentaro Takahashi/Bloomberg

Nestle is taking an innovative approach to product development, starting with the Japan market.

According to Quartz News – “Some 100,000 people are taking part in a company program there that gives consumers a kit to collect their DNA at home. The program also encourages them to use an app to post pictures of what they’re eating. Nestlé then recommends dietary changes and supplies specialized supplements that can be sprinkled on or mixed into a variety of food products, including teas.”

Ultimately, the goal for Nestle actually goes beyond this, to creating completely individualized products based on individuals’ DNA that could even be designed to prevent serious diseases like cancer. Quartz’s crude example is “Pizzas that can ward off Alzheimer’s disease, for instance”.

One nutritional scientist says, “This is going to be the manifestation of the future. The one-size-fits-all platform is a thing of the past.”

Why it’s hot:

First, as the largest food company in the world, Nestle could be leading the way into a new era of food production – one that’s almost completely the opposite of its heritage over the last few decades. But most importantly, it’s another example of the shift we’re finally seeing from mass production to ultra-personalized products. While using DNA as the mechanism is not without concerns, what better experience than having food and supplements created for you based on what your body needs to keep you at peak health.

[Source]

Your Kid’s Computer Has Dinner Covered.

Neural networks are computer learning algorithms that mimic the interconnected neurons of a living brain, managing astonishing feats of image classification, speech recognition, or music generation by forming connections between simulated neurons.

I’m not a neural network expert, so I had to look that one up when I heard that there was a grad student who loaded a neural network code on her 2010 MacBook Pro, and started training it on a bunch of recipes and cocktails.

Here’s a few recipes the network has generated:

Pears Or To Garnestmeam

meats

¼ lb bones or fresh bread; optional
½ cup flour
1 teaspoon vinegar
¼ teaspoon lime juice
2  eggs

Brown salmon in oil. Add creamed meat and another deep mixture.

Discard filets. Discard head and turn into a nonstick spice. Pour 4 eggs onto clean a thin fat to sink halves.

Brush each with roast and refrigerate.  Lay tart in deep baking dish in chipec sweet body; cut oof with crosswise and onions.  Remove peas and place in a 4-dgg serving. Cover lightly with plastic wrap.  Chill in refrigerator until casseroles are tender and ridges done.  Serve immediately in sugar may be added 2 handles overginger or with boiling water until very cracker pudding is hot.

Yield: 4 servings

This is from a network that’s been trained for a relatively long time – starting from a complete unawareness of whether it’s looking at prose or code, English or Spanish, etc, it’s already got a lot of the vocabulary and structure worked out.

This is particularly impressive given that it has the memory of a goldfish – it can only analyze 65 characters at a time, so by the time it begins the instructions, the recipe title has already passed out of its memory, and it has to guess what it’s making. It knows, though, to start by browning meat, to cover with plastic wrap before chilling in the refrigerator, and to finish by serving the dish.

Compare that to a recipe generated by a much earlier version of the network:

Immediately Cares, Heavy Mim

upe, chips

3  dill loasted substetcant
1  cubed chopped  whipped cream
3  unpreased, stock; prepared; in season
1  oil
3 cup milk
1 ½ cup mOyzanel chopped
½ teaspoon lemon juice
1 ¼ teaspoon chili powder
2 tablespoon dijon stem – minced
30  dates afrester beater remaining

Bake until juice. Brush from the potato sauce: Lightly butter into the viscin. Cook combine water. Source: 0 25 seconds; transfer a madiun in orenge cinnamon with electres if the based, make drained off tala whili; or chicken to well. Sprinkle over skin greased with a boiling bowl.  Toast the bread spritkries.

Yield: 6 servings

which bakes first, has the source in the middle of the recipe directions, mixes sweet and savory, and doesn’t yet know that you can’t cube or chop whipped cream.

An even earlier version of the network hasn’t yet figured out how long an ingredients list should be; it just generates ingredients for pages and pages:

Tued Bick Car

apies

2 1/5 cup tomato whene intte
1 cup with (17 g cas pans or
½ cup simmer powder in patsorwe ½ tablespoon chansed in
1 ½ cup nunabes baste flour fite (115 leclic
2 tablespown bread to
¼ cup 12″. oz mice
1  egg barte, chopped shrild end
2 cup olasto hote
¼ cup fite saucepon; peppen; cut defold
12 cup mestsentoly speeded boilly,, ( Hone
1  Live breseed
1  22 ozcugarlic
1 cup from woth a soup
4 teaspoon vinegar
2 9/2 tablespoon pepper garlic
2 tablespoon deatt

And here’s where it started out after only a few tens of iterations:

ooi eb d1ec Nahelrs  egv eael
ns   hi  es itmyer
aceneyom aelse aatrol a
ho i nr  do base
e2
o cm raipre l1o/r Sp degeedB
twis  e ee s vh nean  ios  iwr vp  e
sase
pt e
i2h8
ePst   e na drea d epaesop
ee4seea .n anlp
o s1c1p  ,  e   tlsd
4upeehe
lwcc   eeta  p ri  bgl as eumilrt

Even this shows some progress compared to the random ASCII characters it started with – it’s already figured out that lower case letters predominate, and that there are lots of line breaks. Pretty impressive!

Why It’s Hot:
Progress, progress, progress. Sometimes we take for granted how long and arduous the road to further our convenience is, or how well-equipped technology actually gets us from point A to B. We don’t always need to look under that hood, but we should be happy someone does, and technology such as machine learning neural networks continue to evolve to make our lives easier-or more entertaining until they get something right.  As the ability to learn from the tons of content mankind has already created continues to improve, there really is some scary (don’t)DIY frontiers on the horizon. Forget about wondering if your kid lifted their essay content from an online wiki source, worry instead if he loaded a code, taught his Mac to ingest thousands of volumes of American history and spit out a dissertation on the significance of the Lincoln-Douglass debates without penning a word. Then don’t punish that kid, get him a job making me new cocktails.
Click here if you want to see the cocktails it created:

If salad ingredients and robots made love…

It would be a company called Chowbotics. They just landed $5 mil in Series A, further developing the food service robotic industry.

Its flagship product is called Sally, a salad-making robot that uses 20 different food canister to prepare and serve more than 1,000 types of salad. Number of pilot customers have signed on- restaurants, co-working spaces, and corporate cafeterias.

Benefits :

  • Sally-made salads can be precisely measured – know exactly how many calories are going into your food.
  • Data-driven platform can measure both popularity of specific recipes, # of caloric intake, increase or decline of demand on ingredients  – all that can help both healthcare and the food industry make better informed decisions.